Improved Local Indicators of Spatial Association Analysis for Zero-Heavy Crack Cocaine Seizure Data

Eunseong Jang (), Margrét V. Bjarnadóttir (), Marcus Boyd () and S. Raghavan ()
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Eunseong Jang: The Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Margrét V. Bjarnadóttir: The Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Marcus Boyd: Applied Research Lab for Intelligence & Security (ARLIS), University of Maryland, College Park, Maryland 20742
S. Raghavan: The Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742; and Institute for Systems Research, University of Maryland, College Park, Maryland 20742

INFORMS Joural on Data Science, 2026, vol. 5, issue 2, 81-101

Abstract: Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context, the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data. We then propose an improved LISA analysis method for spatial data with a majority of zero values. This work constitutes a possible path to a more appropriate understanding of the underlying spatial relationships. Applying our proposed methodology to crack cocaine seizure data in the United States, we show how our improved methods identify different spatial patterns, which in our context could lead to different real-world law enforcement strategies. As LISA analysis is a popular statistical approach that supports policy analysis and design, and as zero-heavy data are common in these scenarios, we provide a framework that is tailored to zero-heavy contexts, improving interpretations and providing finer categorization of observed data, ultimately leading to better decisions in multiple fields where spatial data are foundational.

Keywords: exploratory spatial data analysis; LISA analysis; zero-heavy data; local Moran’s I; U.S. cocaine seizures (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:5:y:2026:i:2:p:81-101

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